Data Modeling and Storage Architectures

This unit covers principles and practices for designing data models, selecting storage architectures, and constructing reliable data pipelines. Students will learn normalization and denormalization strategies, compare databases, data warehouses, data lakes, and lakehouses, build and automate ETL pipelines (Apache Airflow), and assess data quality to mitigate bad data—preparing them to operationalize data solutions in subsequent units.

Learning Objectives

• Analyze business and analytical requirements to design relational schemas using normalization and denormalization techniques appropriate for performance and storage trade-offs
• Apply data modeling best practices and database optimization strategies to create efficient schema designs and indexing choices for sample datasets
• Demonstrate the differences and appropriate use cases for databases, data warehouses, data marts, data lakes, and lakehouses and select the best storage architecture for given scenarios
• Evaluate and build end-to-end data pipelines by implementing batch and stream patterns, employing orchestration (Apache Airflow), and applying data quality checks to detect and mitigate bad data